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Outlier Detection Using Minimum Vector Variance Algorithm with Depth Function and Mahalanobis Distance: Bahasa Indonesia

Bahasa Indonesia: Bahasa Indonesia

Jurnal Matematika, Statistika dan Komputasi · Vol. 17 No. 3 (2021) · pp. 418–427

DOI
10.20956/j.v17i3.12629
ISSN
2614-8811, 1858-1382
Published
2021-05-12
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

English

Outliers are observations where the point of observation deviates from the data pattern. The existence of outliers in the data can cause irregularities in the results of data analysis. One solution to this problem is to detect outliers using a statistical approach. The statistical approach method used in this study is the Minimum Vector Variance (MVV) algorithm which has robust characteristics for outliers. The purpose of this research is to detect outliers using the MVV algorithm by changing the data sorting criteria using the Robust Depth Mahalanobis to produce maximum detection. The results obtained from this study are that RDMMVV is superior to the observed value in showing the outliers and the location of the outliers in the data plot compared to DMMVV and MMVV.

Bahasa Indonesia

Pencilan merupakan suatu pengamatan yang titik pengamatannya menyimpang dari pola data. Keberadaan pencilan pada data dapat mengakibatkan penyimpangan terhadap hasil analisis data. Salah satu solusi untuk permasalahan tersebut adalah dengan pendeteksian pencilan menggunakan pendekatan statistik. Metode pendekatan statistik yang digunakan dalam penelitian ini adalah algoritma Minimum Vector Variance (MVV) yang memiliki sifat robust terhadap pencilan. Tujuan dalam penelitian ini adalah mendeteksi pencilan menggunakan algoritma MVV dengan mengubah kriteria pengurutan data menggunakan Robust Depth Mahalanobis untuk menghasilkan pendeteksian yang maksimal. Hasil yang diperoleh dari penelitian ini adalah RDMMVV lebih unggul dari nilai observasi dalam menunjukkan pencilan serta letak pencilan dalam plot data dibanding DMMVV dan MMVV.

Keywords

Collaboration network of the authors

40 authors · 134 co-authorship links · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Sultan Ageng Tirtayasa University
  • Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Georgina Maria TinungkiAffiliation unknown5
Nasrah SirajangAffiliation unknown4
Andi Isna YunitaAffiliation unknown3
Sitti SahrimanAffiliation unknown2
Anisa AnisaSultan Ageng Tirtayasa University2
Siswanto SiswantoAffiliation unknown2
Saleh AFAffiliation unknown2
Georgina M. TinungkiAffiliation unknown2
Riska RasyidAffiliation unknown2
Andi Kresna JayaAffiliation unknown2
Nirwan IlyasAffiliation unknown2
Raupong RaupongAffiliation unknown1
Vieri KoerniawanAffiliation unknown1
Rizki AdiputraAffiliation unknown1
Ika FitrianaAffiliation unknown1
Anna IslamiyatiAffiliation unknown1
Novilia JaoAffiliation unknown1
Ratmila MammiAffiliation unknown1
Aqilah Salsabila RahmanAffiliation unknown1
Ika Pratiwi HayaAffiliation unknown1
Nurhidaya LAffiliation unknown1
Sonya SonyaAffiliation unknown1
Rael Hofni TandirerungAffiliation unknown1
Nuralyatussa’ adaAffiliation unknown1
- SiswantoAffiliation unknown1
Ayu Pratika PutriAffiliation unknown1
Annurial Fitrayah TaufiqAffiliation unknown1
Ahmad Maulana SyarifAffiliation unknown1
Nurmimie KharinaAffiliation unknown1
Samsir Aditya AniaAffiliation unknown1
Alimatun NajihaAffiliation unknown1
Fadhila Febriyanti NajamuddinAffiliation unknown1
Mayashari MayashariAffiliation unknown1
Herlina RasyidUniversitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi1
Nur Aliah RamadhaniAffiliation unknown1
Nadia Nazwadiah CaesarAffiliation unknown1
Zaraswati Dwyana ZainuddinAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Outlier Detection Using Minimum Vector Variance Algorithm with Depth Function and Mahalanobis Distance: Bahasa Indonesia</dc:title>
	<dc:title xml:lang="id">Bahasa Indonesia: Bahasa Indonesia</dc:title>
	<dc:creator xml:lang="en">Sari, Puji Puspa</dc:creator>
	<dc:creator xml:lang="en">Herdiani, Erna Tri </dc:creator>
	<dc:creator xml:lang="en">Sunusi, Nurtiti </dc:creator>
	<dc:subject xml:lang="en">Depth</dc:subject>
	<dc:subject xml:lang="en">Mahalanobis</dc:subject>
	<dc:subject xml:lang="en">MVV</dc:subject>
	<dc:subject xml:lang="en">Pencilan</dc:subject>
	<dc:subject xml:lang="id">Bahasa Indonesia</dc:subject>
	<dc:subject xml:lang="id">Bahasa Inggris</dc:subject>
	<dc:description xml:lang="en">Outliers are observations where the point of observation deviates from the data pattern. The existence of outliers in the data can cause irregularities in the results of data analysis. One solution to this problem is to detect outliers using a statistical approach. The statistical approach method used in this study is the Minimum Vector Variance (MVV) algorithm which has robust characteristics for outliers. The purpose of this research is to detect outliers using the MVV algorithm by changing the data sorting criteria using the Robust Depth Mahalanobis to produce maximum detection. The results obtained from this study are that RDMMVV is superior to the observed value in showing the outliers and the location of the outliers in the data plot compared to DMMVV and MMVV.</dc:description>
	<dc:description xml:lang="id">Pencilan merupakan suatu pengamatan yang titik pengamatannya menyimpang dari pola data. Keberadaan pencilan pada data dapat mengakibatkan penyimpangan terhadap hasil analisis data. Salah satu solusi untuk permasalahan tersebut adalah dengan pendeteksian pencilan menggunakan pendekatan statistik. Metode pendekatan statistik yang digunakan dalam penelitian ini adalah algoritma Minimum Vector Variance (MVV) yang memiliki sifat robust terhadap pencilan. Tujuan dalam penelitian ini adalah mendeteksi pencilan menggunakan algoritma MVV dengan mengubah kriteria pengurutan data menggunakan Robust Depth Mahalanobis untuk menghasilkan pendeteksian yang maksimal. Hasil yang diperoleh dari penelitian ini adalah RDMMVV lebih unggul dari nilai observasi dalam menunjukkan pencilan serta letak pencilan dalam plot data dibanding DMMVV dan MMVV.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2021-05-12</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/12629</dc:identifier>
	<dc:identifier>10.20956/j.v17i3.12629</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 17 No. 3 (2021):  May, 2021; 418-427</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/12629/6713</dc:relation>
	<dc:relation>10.20956/j.v17i3.12629.g6713</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2021 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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